Technical Field
This disclosure relates to medical imaging, including dynamic imaging using positron emission tomography (PET) scanners that produce and process list-mode PET data collected in a dynamic protocol.
Description of Related Art
PET scanners may be used for both biomedical and neuro-scientific research and clinical studies for diagnosing and staging disease and for assessing response to therapy using radiotracers labelled with positron emitting isotopes.
The radioactive tracer may be injected before or at the start of the PET scan. One of the most commonly used radiotracers is 18F-FDG (2-deoxy-2-(18F) fluoro-D-glucose), which is a glucose analog. FDG may be used as an example, but PET can be applied with any tracer labelled with a positron emitting isotope.
Positrons may be produced from the nucleus of the radioisotopes. The annihilation of the positron with an electron after it travels a short range may produce a pair of high energy (511 keV) photons. This pair of photons may travel in approximately opposite directions, and may be detected by detectors that surround the patient (FIG. 1).
A photon pair arriving at two different detectors within a short time window may be called a coincidence or an event and may be assumed to come from the same annihilation. The difference in arrival time may also be recorded for PET scanners operating in time-of-flight model. The annihilation may be assumed to happen along the line connecting the two detectors, commonly referred to as the “line of response” (LOR). FIG. 1 illustrates examples of these as lines of response 101 and 103. List-mode data from the scanner may include a record of the LOR for each detected event and the time at which the event occurred. The PET image may be reconstructed directly from this list-mode data or the list-mode data may first be sorted into sinograms from which the PET image is reconstructed.
Traditional or static PET images may represent accumulated tracer uptake computed from PET data acquired over a single contiguous time interval, typically starting about one hour after the tracer is administered. To form static PET images that cover an axial length 201 (FIG. 2) of the patient that is longer than the axial extent 202 (FIG. 2) of the scanner, the patient lies on a bed 203 that is moved through the scanner as part of the data acquisition protocol so that time-contiguous data are acquired from each part of the patient to be imaged. The protocol may use a step and shoot mode in which the bed 203 remains stationary in each position while data is acquired. Alternatively, the bed 203 may undergo continuous rectilinear motion during data acquisition. See U.S. Pat. No. 6,915,004 B2, Newport D F, Casey M E, Luk W K, Reed J H (2002).
Static PET images may not make use of the information contained in the dynamic changes in tracer uptake which can be useful in diagnosing, staging disease, and assessing response to therapy. Changes in uptake may be assessed by comparing two images of the patient acquired over two different time intervals. See H. Zhuang, M. Pourdehnad, E. S. Lambright, A. J. Yamamoto, M. Lanuti, P. Li, P. D. Mozley, M. D. Rossman, S. M. Albelda, and A. Alavi (2001), “Dual Time Point 18F-FDG PET Imaging for Differentiating Malignant from Inflammatory Processes.” J Nucl Med, 42(9): p. 1412-1417. This approach may use a two-pass scan over an axial extent that can exceed that of the scanner. The images may be reconstructed independently from the data from each pass and then compared.
In order to fit an explicit kinetic model, kinetic PET data may be continuously collected from the time the tracer is administered and then fit to a kinetic model that represents dynamic behavior through transfer between a series of compartments (FIG. 3). The rates (K_1,k_2,k_3,k_4) at which this transfer occurs constitute the parameters of the model, see Carson, R. E. (2002), “Tracer kinetic modeling in PET”, Positron Emission Tomography, Basic Science and Clinical Practice, pp. 127-159.
The kinetic models may be fit to a sequence of previously reconstructed images with one set of parameters computed from image values averaged over a user-selected region of interest in the image. These models may represent tracer uptake in terms of quantitative rate parameters and have the potential for improved clinical value over static PET but represent an average over a relatively large volume of tissue.
An alternative approach is to produce an image of one or more rate parameters by fitting the PET data to the kinetic model at each voxel in the image. See Snyder D. L. (1984), “Parameter Estimation for Dynamic Studies in Emission-Tomography Systems Having List-Mode Data”, IEEE Transactions on Nuclear Science, vol. 31(2): pp. 925-931. Several computational methods have been described for this purpose See Kamasak, M. E., Bouman, C. A., Morris, E. D., Sauer, K. (2005), “Direct reconstruction of kinetic parameter images from dynamic PET data”, IEEE Transactions on Medical Imaging, 2005. vol. 24(5): pp. 636-650 and Wang, G., Qi, J. (2009), “Generalized Algorithms for Direct Reconstruction of Parametric Images From Dynamic PET Data”, IEEE Transactions on Medical Imaging, pp. 1717-1726.
These methods for computing parametric images from dynamic PET data may assume that continuous data is acquired from the time that the PET tracer is administered to the patient. This would therefore precludes parametric imaging of regions of the patient's body that are longer than the axial extent 202 of the scanner since the data can only be acquired for one position of the patient in the scanner.
Alternative simplified linear graphical models have been developed to compute approximate dynamic solutions. These include the Patlak model, which is a simplified kinetic model for irreversibly bound tracers that is linear with respect to its parameters, see Patlak, C. S., Blasberg, R. G., and Fenstermacher, J. D. (1983), “Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data”; Journal of Cerebral Blood Flow and Metabolism, vol. 2(1): pp. 1-7), and the Logan plot, for reversible tracers, see J. Logan, J. S. Fowler, N. D. Volkow, A. P. Wolf, S. L. Dewey, D. J. Schlyer, R. R. MacGregor, R. Hitzemann, B. Bendriem, S. J. Gatley, D. R. Christman (1990), “Graphical analysis of reversible radiolig and binding from time-activity measurements applied to [N-11C-methyl]-(−)-cocaine PET studies in human subjects,” Journal of Cerebral Blood Flow and Metabolism 10 (5): 740-747.
A method for voxel-wise reconstruction of images of Patlak parameters is described by Wang et al. see Wang G., Fu, L., Qi, J. (2008), “Maximum a posteriori reconstruction of Patlak parametric image from sinograms in dynamic PET”, Physics in Medicine and Biology, vol. 53: pp. 593-604). This method uses a preconditioned conjugate gradient method applied to sinogram data from a sequence of frames in a single bed position.
An alternative method fits a whole body Patlak model to previously reconstructed images at each bed position. See Karakatsanis N. A., Lodge M. A., Tahari A. K., Zhou Y., Wahl R. L., R. A. (2013), “Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application”, Physics in Medicine and Biology, vol. 58(29): pp. 7391-7418. This method uses multiple passes of the patient and bed through the scanner using multiple bed positions to cover an axial extent that exceeds that of the scanner. The method does not compute the Patlak images directly from the list-mode or sinogram data but first reconstructs images from each pass using the OSEM (ordered subsets expectation maximization) algorithm and then fits the Patlak model at each voxel to these images to form a quantitative image of the Patlak slope and intercept.
These approaches do not describe methods for quantitative Patlak image reconstruction directly from multiple bed-position PET data for use in cases where the extent of the subject to be imaged exceeds the axial length of the scanner. While they analyze changes in radiotracer uptake over time using two or more passes through a scanner, each pass consisting of multiple bed positions, this analysis is applied to images that have already been separately reconstructed from the data from each pass. There may be advantages in terms of image resolution, noise properties and diagnostic power, to directly reconstructing quantitative multiple bed-position Patlak images directly from list-mode or sinogram data from two or more passes.